
where the two terms in the last display are interpreted as the fitting-stability trade-off [1]}. Indeed, the first term corresponds to the average sensitivity of the estimator to the perturbation in one point of the sample and is called the average stability. The second term shows how the empirical loss of the estimator compares to the empirical loss of the best linear predictor \(\omega ^*\)  and is called the fitting term. The remainder of the proof is devoted to the analysis of these two terms.
